A General-Purpose Tagger with Convolutional Neural Networks

We present a general-purpose tagger based on convolutional neural networks (CNN), used for both composing word vectors and encoding context information. The CNN tagger is robust across different tagging tasks: without task-specific tuning of hyper-parameters, it achieves state-of-the-art results in part-of-speech tagging, morphological tagging and supertagging. The CNN tagger is also robust against the out-of-vocabulary problem, it performs well on artificially unnormalized texts

AMR Dependency Parsing with a Typed Semantic Algebra

We present a semantic parser for Abstract Meaning Representations which learns to parse strings into tree representations of the compositional structure of an AMR graph. This allows us to use standard neural techniques for supertagging and dependency tree parsing, constrained by a linguistically principled type system. We present two approximative decoding algorithms, which achieve state-of-the-art accuracy and outperform strong baselines.Comment: This paper will be presented at ACL 2018 (see https://acl2018.org/programme/papers/

The Role of Linguistic Features in Domain Adaptation: TAG Parsing of Questions

The analysis of sentences outside the domain of the training data poses a challenge for contemporary syntactic parsing. The Penn Treebank corpus, commonly used for training constituency parsers, systematically undersamples certain syntactic structures. We examine parsing performance in Tree Adjoining Grammar (TAG) on one such structure: questions. To avoid hand-annotating a new training set including out-of-domain sentences, an expensive process, an alternate method requiring considerably less annotation effort is explored. Our method is based on three key ideas: First, pursuing the intuition that “supertagging is almost parsing” (Bangalore and Joshi, 1999), the parsing process is decomposed into two distinct stages, supertagging and stapling. Second, following Rimell and Clark (2008), the supertagger is trained with an extended dataset including questions, and the resultant supertags are used with an unmodified parser. Third, to maximize improvements gained from additional training of the supertagger, the parser is provided with linguistically-significant features that reflect commonalities across supertags. This novel combination of ideas leads to an improvement in question parsing accuracy of 13% LAS. This points to the conclusion that adaptation of a parser to a new domain can be achieved with limited data through the careful integration of linguistic knowledge

Structural generalization in COGS: Supertagging is (almost) all you need

In many Natural Language Processing applications, neural networks have been found to fail to generalize on out-of-distribution examples. In particular, several recent semantic parsing datasets have put forward important limitations of neural networks in cases where compositional generalization is required. In this work, we extend a neural graph-based semantic parsing framework in several ways to alleviate this issue. Notably, we propose: (1) the introduction of a supertagging step with valency constraints, expressed as an integer linear program; (2) a reduction of the graph prediction problem to the maximum matching problem; (3) the design of an incremental early-stopping training strategy to prevent overfitting. Experimentally, our approach significantly improves results on examples that require structural generalization in the COGS dataset, a known challenging benchmark for compositional generalization. Overall, our results confirm that structural constraints are important for generalization in semantic parsing.Comment: accepted at EMNLP 202